AI Agent Development

Overview

Specialized workflow for building AI agents including single autonomous agents, multi-agent systems, agent orchestration, tool integration, and human-in-the-loop patterns.

When to Use This Workflow

Use this workflow when:

• -Building autonomous AI agents
• -Creating multi-agent systems
• -Implementing agent orchestration
• -Adding tool integration to agents
• -Setting up agent memory

Workflow Phases

Phase 1: Agent Design

#### Skills to Invoke

• -ai-agents-architect - Agent architecture
• -autonomous-agents - Autonomous patterns

#### Actions

1. Define agent purpose

2. Design agent capabilities

3. Plan tool integration

4. Design memory system

5. Define success metrics

#### Copy-Paste Prompts

Use @ai-agents-architect to design AI agent architecture

Phase 2: Single Agent Implementation

#### Skills to Invoke

• -autonomous-agent-patterns - Agent patterns
• -autonomous-agents - Autonomous agents

#### Actions

1. Choose agent framework

2. Implement agent logic

3. Add tool integration

4. Configure memory

5. Test agent behavior

#### Copy-Paste Prompts

Use @autonomous-agent-patterns to implement single agent

Phase 3: Multi-Agent System

#### Skills to Invoke

• -crewai - CrewAI framework
• -multi-agent-patterns - Multi-agent patterns

#### Actions

1. Define agent roles

2. Set up agent communication

3. Configure orchestration

4. Implement task delegation

5. Test coordination

#### Copy-Paste Prompts

Use @crewai to build multi-agent system with roles

Phase 4: Agent Orchestration

#### Skills to Invoke

• -langgraph - LangGraph orchestration
• -workflow-orchestration-patterns - Orchestration

#### Actions

1. Design workflow graph

2. Implement state management

3. Add conditional branches

4. Configure persistence

5. Test workflows

#### Copy-Paste Prompts

Use @langgraph to create stateful agent workflows

Phase 5: Tool Integration

#### Skills to Invoke

• -agent-tool-builder - Tool building
• -tool-design - Tool design

#### Actions

1. Identify tool needs

2. Design tool interfaces

3. Implement tools

4. Add error handling

5. Test tool usage

#### Copy-Paste Prompts

Use @agent-tool-builder to create agent tools

Phase 6: Memory Systems

#### Skills to Invoke

• -agent-memory-systems - Memory architecture
• -conversation-memory - Conversation memory

#### Actions

1. Design memory structure

2. Implement short-term memory

3. Set up long-term memory

4. Add entity memory

5. Test memory retrieval

#### Copy-Paste Prompts

Use @agent-memory-systems to implement agent memory

Phase 7: Evaluation

#### Skills to Invoke

• -agent-evaluation - Agent evaluation
• -evaluation - AI evaluation

#### Actions

1. Define evaluation criteria

2. Create test scenarios

3. Measure agent performance

4. Test edge cases

5. Iterate improvements

#### Copy-Paste Prompts

Use @agent-evaluation to evaluate agent performance

Agent Architecture

User Input -> Planner -> Agent -> Tools -> Memory -> Response
              |          |        |        |
         Decompose   LLM Core  Actions  Short/Long-term

Quality Gates

• -[ ] Agent logic working
• -[ ] Tools integrated
• -[ ] Memory functional
• -[ ] Orchestration tested
• -[ ] Evaluation passing

Related Workflow Bundles

• -ai-ml - AI/ML development
• -rag-implementation - RAG systems
• -workflow-automation - Workflow patterns